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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Cursor AI: Why You Should Try it Once?

Pankaj Singh
Pankaj Singh Last Updated : 27 Aug, 2024
7 min read

Introduction

After Andrej Karpathy’s viral tweet, “English has become the new programming language, ” here is another trending tweet on X saying, “ Future be like Tab Tab Tab.”  You might be wondering what reference he is talking about! Is some tool coming, or is this just a playful nod to how we interact with code today?

Let’s find out!!

Have you ever imagined turning your basic coding skills into a powerful tool for creating full-fledged applications with seamless front and robust back ends? If so, then Cursor AI is here for you. AI is an evolving technology at breakneck speed, and what once seemed impossible is now within your grasp. Thanks to technological advancements, you can now communicate with AI in natural language to bring your ideas to life without getting stuck in complex code. Now, Tab, Tab, Tab with Cursor AI is the new way to build applications without much coding experience, and it is far better than GitHub Copilot.

Cursor AI

Overview

  • Cursor AI empowers developers to create full-fledged applications using natural language and predictive coding.
  • Cursor AI makes coding as simple as pressing TAB, automating much of the development process.
  • Seamless integration, natural language editing, and advanced features make Cursor AI a revolutionary tool for developers.
  • Cursor AI prioritizes privacy and security, offering a safe code development environment without compromising functionality.
  • Praised by developers worldwide, Cursor AI redefines the coding experience, making it faster, easier, and more intuitive.

Table of contents

What is Cursor AI?

Cursor AI is not just another code editor; it’s a revolution in how we approach coding. Built to make developers extraordinarily productive, Cursor takes the concept of AI-assisted coding to a whole new level. Gone are the days of manually writing and debugging every line of code. With Cursor, you hit TAB, and your code practically writes itself.

What can Developers do with Cursor AI?

Here’s how Cursor AI is helpful for Developers:

1. Seamless Integration with Your Codebase

The Cursor is designed to understand your codebase intimately. Whether you’re working on a small project or a large-scale application, Cursor adapts to your environment, offering the best possible suggestions based on the context. It’s like having a personal coding assistant who knows your project inside out. No more searching through endless documentation or second-guessing your syntax—Cursor has you covered.

2. Natural Language Editing

One of Cursor’s most powerful features is its ability to edit code using natural language instructions. Need to update an entire class or refactor a function? Simply type your request, and Cursor will do the rest. It’s like conversing with your code, where your ideas are instantly transformed into working code. This feature saves time and makes coding more intuitive and accessible, especially for those who may not have extensive coding experience.

3. Predictive Coding with TAB

Cursor’s magic lies in its TAB functionality. By predicting your next move, Cursor allows you to breeze through coding tasks quickly. Each time you press TAB, you’re one step closer to completing your project. This feature is especially useful in repetitive tasks, where Cursor’s predictive capabilities can reduce work hours to mere minutes. It’s coding at the speed of thought.

4. Familiar Yet Advanced

For those accustomed to traditional code editors, Cursor feels familiar yet advanced. You can import all your extensions, themes, and keybindings with just one click, ensuring a smooth transition from other editors like VS Code. This blend of familiarity and innovation makes Cursor a tool and an extension of your existing workflow.

5. Privacy and Security at Its Core

Cursor takes your security seriously in the age of data breaches and privacy concerns. With a dedicated privacy mode, the platform does not store your code. Cursor is SOC 2 certified, ensuring that your work remains private and secure. Additionally, you can bring your API key for a personalized experience or use the hosted version for full functionality. Cursor respects your privacy while enhancing your productivity.

6. Autocomplete with Multi-Line Edits

6. Autocomplete with Multi-Line Edits

Cursor AI offers a robust autocomplete feature that goes beyond simple word suggestions. It predicts your next edit across multiple lines, considering your recent changes. This feature is always on, helping you build software faster by suggesting edits that align with your coding patterns and recent activities.

7. Intelligent Code Generation and Smart Rewrites

7. Intelligent Code Generation and Smart Rewrites

Cursor AI excels in both code generation and error correction. Its intelligent code generation capability is context-aware, understanding recent changes in your code to predict your next steps and suggest multiple edits at once. This streamlines your workflow and saves time. Simultaneously, the smart rewrites feature addresses mistakes by automatically correcting careless typing errors, ensuring your code remains clean and error-free. Together, these features enhance productivity and maintain code quality effortlessly.

8. Command K for Fast Edits and Terminal Commands

8. Command K for Fast Edits and Terminal Commands

The Ctrl K shortcut is a powerful tool in Cursor AI, enabling you to edit or write code with AI assistance. Ctrl K handles it all, whether you want to edit existing or generate new code from scratch. Additionally, you can use Ctrl K in the terminal to convert plain English commands into terminal commands, simplifying your workflow.

Moreover, Cursor AI offers a suite of other features to streamline coding. It includes predictive code navigation, smart rewriting for error correction, and interactive chat for real-time codebase queries. Additionally, it allows seamless integration of code suggestions and supports terminal command conversion through intuitive commands.

Also read: 12 Best AI Coding Assistants You Must Try in 2024

Cursor AI: A Leap Forward to AI Coding

Cursor AI, an AI-powered code editor built on VS Code, has quickly gained popularity among developers for its ability to enhance productivity. Many developers tested its capabilities and found it to be highly effective in various tasks, including full-stack application development, data analysis, and code review.

Key features include AI-powered chat, inline chat, and adding entire codebases as context, which make the development process faster and more efficient. Cursor also excels at automating tasks like testing, documentation, and code review, saving developers significant time.

Cursor AI Pricing

Cursor AI stands out from competitors like GitHub Copilot due to its seamless integration and richer contextual awareness. Priced at $20 per month or $16 annually, it offers good value for its capabilities. Overall, Cursor AI is a powerful tool that bridges the gap between imagination and reality in software development.

Also read: 12 AI Code Generator Tools to Boost Your Programming Skills

Loved by Developers Worldwide

Andrej’s perspective highlights a significant shift in how programming is evolving with tools like VS Code Cursor and Sonnet 3.5. He observes that much of modern programming has transitioned from traditional coding to more of a collaborative effort with AI, where the programmer’s role is increasingly about writing clear prompts and guiding the AI to generate code rather than writing all the code manually.

The concept of “half-coding,” where a developer writes the initial part of the code and allows the AI to complete it, represents a new way of thinking about software development. This approach can lead to significant time savings, especially when AI-generated diffs match the developer’s intentions, sometimes producing large chunks of code that would have taken much longer to write manually.

Andrej’s reflection on the rapid pace of change in programming tools suggests that the future of coding might be less about manual effort and more about effective communication with AI models. The idea of getting “sufficiently used to all the features” of these new tools parallels the early days of learning to code, where familiarity and mastery over the tools significantly enhance productivity.

Talking Cursor AI has quickly become a favorite among developers, from startups to large enterprises. Engineers from companies like Shopify, OpenAI, and others have praised the seamless integration of AI into the coding process. It’s more than just a tool; it’s a game-changer redefining how developers approach their work.

Johannes Schickling, founder of Prisma, says, “After many recommendations, I finally switched from VSC to Cursor and… wow! It’s absolutely incredible. If you like Copilot (or don’t), Cursor’ll blow you away. There is no going back.”

These testimonials highlight Cursor AI’s transformative impact across the tech industry. It’s not just about writing code; it’s about elevating the entire coding experience.

Also read: How to solve all your Coding Problems using Blackbox AI?

Conclusion

As AI continues to evolve, tools like Cursor are leading the charge in making coding more accessible, efficient, and powerful. Whether you’re a seasoned developer or just starting, Cursor AI is the key to unlocking new levels of productivity and creativity. So why not give it a try? Download Cursor for free and experience the future of coding, where TAB, TAB, TAB is all it takes to bring your ideas to life.

Frequently Asked Questions

Q1. What is Cursor AI?

Ans. Cursor AI is an AI-powered code editor designed to revolutionize coding by allowing developers to write code using natural language and predictive TAB functionality.

Q2. How does Cursor AI assist with coding?

Ans. Cursor AI integrates seamlessly with your codebase, offers natural language editing, and uses predictive coding with the TAB key to speed up development.

Q3. Is Cursor AI compatible with other code editors?

Ans. Yes, Cursor AI feels familiar to those using traditional code editors and allows you to import extensions, themes, and keybindings from editors like VS Code.

Q4. How does Cursor AI ensure privacy and security?

Ans. Cursor AI is SOC 2 certified and offers a dedicated privacy mode, ensuring that your code is not stored and remains secure.

Q5. What makes Cursor AI different from other tools like GitHub Copilot?

Ans. Cursor AI offers richer contextual awareness, seamless integration with your codebase, and advanced features like natural language editing, making it a more powerful and efficient tool.

AIAI tool for developmentAndrej Karpathycursor aidata analysisData Analyst
Pankaj Singh
Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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